Big Data’s Big Problem

The E&P Vertical Calls Me Out

It’s not easy being a visionary. There’s the constant stress of predicting future trends, of analyzing new ideas and technologies, and of course there’s the groupies. A hard life, to be sure, but it got a little harder for me when I met with an oil/gas supermajor recently. Every idea I had for Big Data and analytics on the upstream side was rejected as no more than a “moon-shot.” Where was my rock-solid financial value proposition, they asked. What was the compelling narrative they could sell to the business? They were drilling for oil to make money, not to exploit new technologies. The idea of maybe saving some money in the future was a non-starter. I was talking about Big Data as a philosophy that would engender a new way of looking at and leveraging data assets; they were looking for use cases that would drive immediate, quantifiable savings.

It’s easy to blame a quarterly earnings report on what I saw as short-sightedness, and it’s even easier to blame an IT director or a VP that is responsible for a P&L for failing to see the long game here: invest a few million now to return tens of millions in a couple of years. But that argument is as laughable as it is insipid; as Donald Rumsfeld once said, “You go to war with the Army you have, not the Army you want.” Although I’m not a big fan of Rumsfeld, this particular statement resonated with me. For that director and for every decision-maker at every other company, it is all about the first nine yards. Unless you are a Nelson Mandela or an Elon Musk, your odds of changing the perspective or the values of entrenched interests are pretty low. Instead of trying to completely change the direction of your client, course-correct by smaller, more achievable goals that are in line with conventional imperatives.

Big Data by definition is big – it’s messy, unruly, coming in from multiple sources with multiple formats that only have one thing in common: they don’t like each other. In the E&P space, the volume and velocity of data is staggering, particularly when you consider the nearly fanatical focus on real time data-in-motion as a tool for decision support. Countless sensors and control/automation systems stream a deluge of telemetry and environmental information to systems that simply don’t know what to do with it. Consequently, most of that data is orphaned. It is (usually) never fed into corporate or analytic systems after the fact. Once it is used, the data is essentially worthless.

Worthless? No so fast.

So you have a lot of drilling data and don’t know what to do with it. Well, imagine what you could do with that data. Mated with other historical information, you could characterize high producing wells. Determine the best drill bit and drill speed to use in specific types of geological features. Identify optimal conditions and angles for fracking. If only you could leverage that data and historical information. And find someone willing to spend a few million dollars on the research and analytics needed to scour the logs of tens of thousands of wells and reservoirs and historical telemetry information in order to put together a reasonable model. In this current environment of billions of dollars of profit instead of tens of billions, prayer might be a better option than a three year ROI model that is south of 15%. And that’s if you can convince your sponsors that you didn’t pull the numbers out of your… hat.

But this does not mean that big data is dead, or that it will be relegated to the arcane and esoteric halls of IBM’s Watson group? Thankfully, no. There are entrepreneurs out there that see a market opportunity and are willing to invest a couple of dollars to see an idea come to fruition. The market demand is there; it’s just a matter of monetizing it.

I met with Lars Olrik and Phil Wade of Verdande Technology, a Big Data player in the E&P space. Instead of trying to find and solve all of the problems that they see in upstream, they decided to hit the most common types of nonproductive time and save their clients a couple of bucks in the process. In a nutshell, they have found the right economics for a Big Data solution – do all the heavy lifting as a part of their R&D, and sell the solution as a packaged RTO decision support engine. The analytic interrelationships are defined in what they call “cases,” a historical record of data patterns as they relate to one of the many situations (or cases, in their vernacular) their engine is continuously looking for. This is done from their own R&D; from the client side, there’s no correlating and statistically analyzing data streams in order to create these cases. Once the tool is plugged into the client, the analytic engine uses case-based reasoning engine to monitor realtime sensor output and look for problem pattern matches and present that warning to the end user. Is your drill string about to twist off? Is a motor about to fail? Looking at sensors in the time and depth domains that aggregate temperature, pressure, volume, weight, revolutions, speed, and others, Verdande will tell you about potential down-hole catastrophes and how similar you are to the event actually occurring according to past experience. To my semi-trained eye, it seems like Verdande wants to take factory drilling from infancy into its teens.

The future of analytics and decision support in the E&P space is a difficult one; real-time analysis of data for decision support can have consequences that run into the millions of dollars a day. Solutions like Verdande look at the problem from a client perspective – how to drive value and decrease NPT on a subscription basis rather than having to invest large sums of time and money into a data science practice. Ultimately, I see a world where Big Data is an integral part of every operation, but Verdande serves as not just a bridge between today’s practice and tomorrow’s promise, but as a guide to those visionaries with the mettle to bind analytics and economics together.